

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__snake_case				:      Optional[Any]   		=			logging.get_logger(__name__)


if is_vision_available():
	import PIL



class        A__(a_							):
						"""simple docstring"""






						_A    :				Dict       					=			['''pixel_values''']





						def __init__(    self  ,      _lowercase = True  ,      _lowercase = None  ,      _lowercase = PILImageResampling.BICUBIC  ,      _lowercase = True  ,      _lowercase = None  ,      _lowercase = True  ,      _lowercase = 1 / 255  ,      _lowercase = True  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = True  ,      **_lowercase  ,      )     -> None:
									super().__init__(**_lowercase    )
									a_						:						Optional[Any] 							= size if size is not None else {"""shortest_edge""": 224}
									a_						:						str 							= get_size_dict(_lowercase  ,      default_to_square=_lowercase    )
									a_						:						Dict 							= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
									a_						:						Tuple 							= get_size_dict(_lowercase  ,      default_to_square=_lowercase  ,      param_name="""crop_size"""    )

									a_						:						Optional[Any] 							= do_resize
									a_						:						List[str] 							= size
									a_						:						Optional[int] 							= resample
									a_						:						List[Any] 							= do_center_crop
									a_						:						int 							= crop_size
									a_						:						Tuple 							= do_rescale
									a_						:						Any 							= rescale_factor
									a_						:						Tuple 							= do_normalize
									a_						:						int 							= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									a_						:						List[Any] 							= image_std if image_std is not None else OPENAI_CLIP_STD
									a_						:						Any 							= do_convert_rgb





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase = PILImageResampling.BICUBIC  ,      _lowercase = None  ,      **_lowercase  ,      )     -> np.ndarray:
									a_						:						Union[str, Any] 							= get_size_dict(_lowercase  ,      default_to_square=_lowercase    )
									if "shortest_edge" not in size:
												raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
									a_						:						List[str] 							= get_resize_output_image_size(_lowercase  ,      size=size["""shortest_edge"""]  ,      default_to_square=_lowercase    )
									return resize(_lowercase  ,      size=_lowercase  ,      resample=_lowercase  ,      data_format=_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      **_lowercase  ,      )     -> np.ndarray:
									a_						:						List[str] 							= get_size_dict(_lowercase    )
									if "height" not in size or "width" not in size:
												raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
									return center_crop(_lowercase  ,      size=(size["""height"""], size["""width"""])  ,      data_format=_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      **_lowercase  ,      )     -> List[Any]:
									return rescale(_lowercase  ,      scale=_lowercase  ,      data_format=_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      **_lowercase  ,      )     -> np.ndarray:
									return normalize(_lowercase  ,      mean=_lowercase  ,      std=_lowercase  ,      data_format=_lowercase  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = ChannelDimension.FIRST  ,      **_lowercase  ,      )     -> PIL.Image.Image:
									a_						:						Tuple 							= do_resize if do_resize is not None else self.do_resize
									a_						:						List[Any] 							= size if size is not None else self.size
									a_						:						Union[str, Any] 							= get_size_dict(_lowercase  ,      param_name="""size"""  ,      default_to_square=_lowercase    )
									a_						:						Dict 							= resample if resample is not None else self.resample
									a_						:						Dict 							= do_center_crop if do_center_crop is not None else self.do_center_crop
									a_						:						Optional[Any] 							= crop_size if crop_size is not None else self.crop_size
									a_						:						Dict 							= get_size_dict(_lowercase  ,      param_name="""crop_size"""  ,      default_to_square=_lowercase    )
									a_						:						Optional[Any] 							= do_rescale if do_rescale is not None else self.do_rescale
									a_						:						Any 							= rescale_factor if rescale_factor is not None else self.rescale_factor
									a_						:						Optional[Any] 							= do_normalize if do_normalize is not None else self.do_normalize
									a_						:						Union[str, Any] 							= image_mean if image_mean is not None else self.image_mean
									a_						:						Union[str, Any] 							= image_std if image_std is not None else self.image_std
									a_						:						Dict 							= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									a_						:						Optional[Any] 							= make_list_of_images(_lowercase    )

									if not valid_images(_lowercase    ):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

									if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""    )

									if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

									if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

									if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
												a_						:						Union[str, Any] 							= [convert_to_rgb(_lowercase    ) for image in images]

									# All transformations expect numpy arrays.
									a_						:						Any 							= [to_numpy_array(_lowercase    ) for image in images]

									if do_resize:
												a_						:						int 							= [self.resize(image=_lowercase  ,      size=_lowercase  ,      resample=_lowercase    ) for image in images]

									if do_center_crop:
												a_						:						int 							= [self.center_crop(image=_lowercase  ,      size=_lowercase    ) for image in images]

									if do_rescale:
												a_						:						str 							= [self.rescale(image=_lowercase  ,      scale=_lowercase    ) for image in images]

									if do_normalize:
												a_						:						Tuple 							= [self.normalize(image=_lowercase  ,      mean=_lowercase  ,      std=_lowercase    ) for image in images]

									a_						:						List[Any] 							= [to_channel_dimension_format(_lowercase  ,      _lowercase    ) for image in images]

									a_						:						str 							= {"""pixel_values""": images}
									return BatchFeature(data=_lowercase  ,      tensor_type=_lowercase    )





from __future__ import annotations


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= 0
			a_						:						str 							= len(a__) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									a_						:						List[Any] 							= i + 1
						else:
									a_						:						Optional[Any] 							= j - 1

			return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")


